Abstract
With the development of civil aviation, the number of flights keeps increasing and the flight delay has become a serious issue and even tends to normality. This paper aims to prove that Stacking algorithm has advantages in airport flight delay prediction, especially for the algorithm selection problem of machine learning technology. In this research, the principle of the Stacking classification algorithm is introduced, the SMOTE algorithm is selected to process imbalanced datasets, and the Boruta algorithm is utilized for feature selection. There are five supervised machine learning algorithms in the first-level learner of Stacking including KNN, Random Forest, Logistic Regression, Decision Tree, and Gaussian Naive Bayes. The second-level learner is Logistic Regression. To verify the effectiveness of the proposed method, comparative experiments are carried out based on Boston Logan International Airport flight datasets from January to December 2019. Multiple indexes are used to comprehensively evaluate the prediction results, such as Accuracy, Precision, Recall, F1 Score, ROC curve, and AUC Score. The results show that the Stacking algorithm not only could improve the prediction accuracy but also maintains great stability.
Highlights
Airports are significant nodes of air transportation. e number of airport flight delays has been on increase in recent years
In the single algorithm comparison, we find that the Random Forest has great performance, and Gaussian Naive Bayes and Logistic Regression perform poorly
We propose a flight delay prediction classification method based on the Stacking algorithm. e SMOTE algorithm is introduced to process imbalanced datasets used, and the Boruta algorithm is utilized to select input features. e Logan International Airport flight data in 2019 are collected to carry out comparative experiments, and the Accuracy, Precision, Recall, and F1 Score are above 0.8
Summary
Airports are significant nodes of air transportation. e number of airport flight delays has been on increase in recent years. With the development of artificial intelligence, machine learning technology has been widely used in airport flight delay prediction. Is paper aims to provide an applicable flight delay classification prediction method, especially for solving algorithm selection problems. Many scholars have studied flight delay issue based on different machine learning methods. Took Lithuania Airport flight delays datasets as the research object and selected seven machine learning algorithms including probabilistic neural network, multilayer perceptron neural network, Gradient-Boosted Tree, Decision Tree, and the GradientBoosted Tree obtained the optimal results [11]. E flight delay prediction methods based on machine learning technology become mature gradually. E experiment result shows that the proposed method effectively processed the complex features and improved the classification accuracy [14].
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